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Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-grey relational analysis

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Defence technology（防务技术） 2019年第4期15卷 545-556页

作者： S.Ajith Arul Daniel R.Pugazhenthi R.Kumar S.Vijayananth Department of Mechanical Engineering Vels Institute of ScienceTechnology and Advanced Studies

This study aims to optimize the input parameters such as mass fraction and particle size of SiC along with depth of cut,feed and cutting speed in the milling of Al5059/SiC/*** hybrid metal matrix composites are generally fabricated by reinforcing of different sizes(10,20,40 μm)of SiC with aluminium at a different levels(5%,10%& 15%)whereas the MoS2 addition is fixed as 2%.The effect of each control factor on response variables are analyzed through Taguchi S/N ratio ***,the most significant method for prediction of response parameters is satisfied by ANN model than the regression *** of variance(ANOVA)results envisage that mass fraction of SiC,feed rate is the most domineering factor on response variable.

关键词： Silicon carbide Temperature Surface roughness Cutting forces Artificial neural network Grey relational analysis

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wasim Haidar SK Wilfred Blessing N. R Sudhakar Kumar Chaubey Zahid Mehmood Kavitha S. J Sutherlin Subitha G College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission.

关键词： Wireless sensor networks Data analysis Smart cities Decision making Throughput Routing protocols Real-time systems Energy efficiency Delays Predictive maintenance

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CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Data

arXiv

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arXiv 2025年

作者： Sumon, Md. Shaheenur Islam Islam, Md. Sakib Bin Rahman, Md. Sohanur Hossain, Md. Sakib Abrar Khandakar, Amith Hasan, Anwarul Murugappan, M. Chowdhury, Muhammad E.H. Department of Electrical Engineering Qatar University Doha2713 Qatar Department of Electrical and Electronic Engineering University of Dhaka Dhaka1000 Bangladesh Department of Industrial and Mechanical Engineering Qatar University Doha2713 Qatar Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha13133 Kuwait Department of Electronics and Communication Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai India

The early detection and prediction of cardiovascular diseases are crucial for reducing the severe morbidity and mortality associated with these conditions worldwide. A multi-headed self-attention mechanism, widely used in natural language processing (NLP), is operated by Transformers to understand feature interactions in feature spaces. However, the relationships between various features within biological systems remain ambiguous in these spaces, highlighting the necessity of early detection and prediction of cardiovascular diseases to reduce the severe morbidity and mortality with these conditions worldwide. We handle this issue with CardioTabNet, which exploits the strength of tab transformer to extract feature space which carries strong understanding of clinical cardiovascular data and its feature ranking. As a result, performance of downstream classical models significantly showed outstanding result. Our study utilizes the open-source dataset for heart disease prediction with 1190 instances and 11 features. In total, 11 features are divided into numerical (age, resting blood pressure, cholesterol, maximum heart rate, old peak, weight, and fasting blood sugar) and categorical (resting ECG, exercise angina, and ST slope). Tab transformer was used to extract important features and ranked them using random forest (RF) feature ranking algorithm. Ten machine-learning models were used to predict heart disease using selected features. After extracting high-quality features, the top downstream model (a hyper-tuned ExtraTree classifier) achieved an average accuracy rate of 94.1% and an average Area Under Curve (AUC) of 95.0%. Furthermore, a nomogram analysis was conducted to evaluate the model's effectiveness in cardiovascular risk assessment. A benchmarking study was conducted using state-ofthe-art models to evaluate our transformer-driven framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Nomograms

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How Do Hackathons Foster Creativity? Towards Automated Evaluation of Creativity at Scale 25

How Do Hackathons Foster Creativity? Towards Automated Evalu...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Jeanette Falk Yiyi Chen Janet Rafner Mike Zhang Johannes Bjerva Alexander Nolte Computer Science Aalborg University Copenhagen Denmark Department of Computer Science Aalborg University Copenhagen Denmark Center for Hybrid Intelligence Department of Management Aarhus University Aarhus Denmark and Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands

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Creating Sodium Ion Channels via De Novo Encapsulation of Ionophores for Enhanced Water Energy Harvesting

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advanced Materials 2025年第20期37卷 e2420636页

作者： Guo, Qing Xing, Zhiwei Guo, Huaxi Lai, Zhuozhi Yi, Jiaming Wu, Di Dai, Zhifeng Zhang, Li Wang, Sai Ma, Shengqian Sun, Qi Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Surface & Interface Science of Polymer Materials of Zhejiang Province School of Chemistry and Chemical Engineering Zhejiang Sci-Tech University Hangzhou310018 China Hangzhou Institute of Advanced Studies Zhejiang Normal University Hangzhou311231 China Department of Chemistry University of North Texas 1508 W Mulberry St DentonTX76201 United States

Biological ion channels achieve remarkable permselectivity and cation discrimination through the synergy of their intricate architectures and specialized ionophores within confined nanospaces, enabling efficient energy conversion. Emulating such selectivity in synthetic nanochannels, however, remains a persistent challenge. To address this, a novel host-guest assembly membrane is developed by incorporating sodium-selective ionophores into a β-ketoenamine-linked covalent organic framework (COF). This design confers exceptional permselectivity and Na+ selectivity, achieving Na+/K+ and Na+/Li+ selectivity ratios of 3.6 and 103, respectively, along with near-perfect Na+/Cl− selectivity under a 0.5 M || 0.01 M salinity gradient. Notably, the membrane dynamically switches its permselectivity to favor anion transport in the presence of high-valent cations (e.g., Ca2+), overcoming limitations such as uphill cation diffusion and back currents observed in conventional cation-selective membranes. This adaptive behavior yields a 4.6-fold increase in output power density in Ca2+-rich environments. These findings advance the design of biomimetic nanochannels with unparalleled ion selectivity and enhanced energy conversion efficiency. © 2025 Wiley-VCH GmbH.

关键词： Ionophores

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GCN-Integrated Multi-Agent Reinforcement Learning (MARL) with Modified Firefly Algorithm for Cross-Layer Optimization in Cognitive Sensor Networks

GCN-Integrated Multi-Agent Reinforcement Learning (MARL) wit...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Karthik Jakranpally Nagabathula Ramesh Babu Doddavarapu Sreenu Babu K. Rakesh Amit Barve Atmaram Shelke Valiant IT Services4000 Stanwick ave Sacramento CA Department of Electronics and Communication Engineering J.B. Institute of Engineering & Technology Hyderabad India Department of Electronics and Communication Engineering Bharatratna Indira Gandhi College of Engineering Solapur India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University P.O.Limda Gujarat India Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

CSN s are integrating cognitive abilities, making it challenging to achieve optimized cross-layer performance with limited resources. Non-coordinated techniques typically demonstrate lower proficiency in dynamic spectrum management and network synchronization. We introduce Graph Convolutional Networks (GCN) and Multi-Agent Reinforcement Learning (MARL) in our architectural solution, with the help of the specially designed Firefly Algorithm. The framework implements a distributed learning process, utilizing GCN for local topology perception and MARL for cooperative spectrum and optimal routing decisions. The results of experimental analysis show large gains compared to traditional methods. The proposed model provided a 94.2% recognition rate in spectrum hole identification, an 89.7% enhancement in network throughput, and a 78.5% diminution in end-to-end delay. The energy efficiency surpasses baseline techniques by 67.3%, while the raw packet delivery reliability remains at 92.8%. Integrating GCN with MARL and the improved Firefly Algorithm can significantly improve cross-layer optimization, offering a promising solution for the next generation of sensor networks.

关键词： Cross layer design Graph convolutional networks Reinforcement learning Throughput Routing Topology Synchronization Reliability Optimization Radio spectrum management

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Detection of Breast Cancer using Quasi-Cross Focused Transformer-Guided Quantum Network with Lotus Optimization

Detection of Breast Cancer using Quasi-Cross Focused Transfo...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Rajesh Munirathnam Vishal Shukla Deepashree Devaraj Chintan Thacker Ujwal Nandekar Natrayan L Data & Analytics Division TechnoGen Inc. Hillsborough New Jersey USA Department of Mechanical Engineering Ramdeobaba College of Engineering and Management Nagpur Maharashtra Electronics and Instrumentation Engineering RV College of Engineering Bengaluru Karnataka Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Post Limda Gujarat Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Pune Symbiosis International (Deemed University) Pune Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai TamilNadu

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Patient outcomes improve if breast cancer is discovered early. Although many deep learning methods have been studied for breast cancer identification, the efficacy of many has been limited. It presents a new method encompassing the Quasi Cross Focused Transformer Guided Quantum Network with Lotus Optimization (QCFT-GQNet-LO) which is novel to resolve these issues. This technique uses Quasi-Cross Filtering (QCF) for image pre-processing, which improves image quality by reducing noise and artefacts. It was evaluated on histopathological images from the MIAS (Mammographic Image Analysis Society) dataset. A Focused Transformer with U-Net (FTUNet) is utilized to precisely segment malignant regions, guaranteeing precise identification of crucial places. The Self-Guided Quantum Network with Triple Attention (SGQNet-TA) controls classification and feature extraction, significantly improving the model's diagnostic abilities. The whole system is optimized using the Lotus Effect Optimization Algorithm (LEOA) to improve the efficiency and robustness. The QCFT-GQNetLO approach is shown to outperform other approaches, with experimentally demonstrated accuracy of 99.9%, and recall rate of 99.8% on the MIAS dataset. This significantly improves diagnostic precision of lesion type categorization and early breast malignancy diagnosis in histopathological images.

关键词： Image segmentation Accuracy Filtering Noise Transformers Breast cancer Robustness Quantum networks Lesions Optimization

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms

A Music Recommender System to Identify the Emotional State o...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Muruganantham A C. Malarvizhi Divya Vahini. S D. Saranya A. Uthiramoorthy Department of Computer Science (PG) Kristu Jayanti College Bengaluru Karnataka India Department of Computer Science PSG Institute of Advanced Studies Coimbatore India Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Department of Information Technology Sri Eshwar College of Engineering Coimbatore Department of Computer Science Rathinam College of Arts and Science

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods--with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be--make tailored entertainment paths in the digital era.

关键词： Support vector machines Emotion recognition Recurrent neural networks Machine learning algorithms Accuracy Mood Face recognition Computational modeling Convolutional neural networks Recommender systems

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Sustainable Cloud-Based Blockchain Solution Integrated with a Supervised Attention Multi-Scale Temporal Convolutional Network for Efficient Supply Chain Management

Sustainable Cloud-Based Blockchain Solution Integrated with ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Nidhi Sharma Alok Sharma Karthik D U Ujwal Prabhakar Nandekar Manisha Maddel Ramya Maranan Computer Engineering The Technological Institute of Textile and Sciences Bhiwani Haryana India Computer Engineering MERI CET Sampla Bahadurgarh India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka India Faculty of Law Symbiosis Centre for Advanced Legal Studies and Research Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of MBA Indira School of Business Studies Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Organizations should prefer SCM ability, as it maintains both operating profit and market competition in dynamic markets. The current methods of analysis make poor real -time paves that give rise to sub -standard decisions that thus reduce the quality of operating performance. Research develops SA-MTCN with DSA to improve the management of the supply chain through better performance properties that deal with current control limits. SA-MTCN supply chain requires monitored attention method and multi-level temporal layers through model design to detect the necessary and sophisticated patterns correctly. The Duck Herd algorithm (DSA) acts as an adaptation system for SA-MTCN Hyperpieter adjustment to produce more stable and efficient model operations. A large data set for the supply chain has established the efficiency of the proposed function by achieving 99.9 % accuracy in evaluation tests. Better predictions emerge from this method as the efficiency of the operational supply chains is increased through time -sensitive data analysis and its functionality informed decision results. Experimental testing resulted in a better performance of the SA-MTCN model when customized by DSA which today offers an efficiently reliable solution supply chain.

关键词： Adaptation models Supply chain management Power measurement Soft sensors Supply chains Prediction algorithms Blockchains Convolutional neural networks Reliability Testing

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Sustainability assessment of carbonaceous nanostructured materials for efficient hydrogen storage

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International Journal of Hydrogen Energy 2025年 135卷 477-498页

作者： Nemati, Behrouz Kamali, Mohammadreza Aminabhavi, Tejraj M. Dewil, Raf Costa, M. Elisabete V. Capela, Isabel Department of Environment and Planning Center for Environmental and Marine Studies CESAM University of Aveiro Aveiro3810-193 Portugal Department of Materials and Ceramics Engineering Aveiro Institute of Materials CICECO University of Aveiro Aveiro3810-193 Portugal KU Leuven Department of Chemical Engineering Process and Environmental Technology Lab J. De Nayerlaan 5 Sint-Katelijne-Waver2860 Belgium Center for Energy and Environment School of Advanced Sciences KLE Technological University Hubballi580 031 India Chandigarh University Punjab Mohali140 413 India Korea University Seoul02841 Korea Republic of University of Oxford Department of Engineering Science Parks Road OxfordOX1 3PJ United Kingdom

Increasing demands for energy driven by economic growth and development and challenges have prompted countries to shift towards renewable energy production. Hydrogen (H2) is a promising energy carrier to address the increasing need for clean energy sources. Hence, efforts are concentrated on developing sustainable methods for efficient production and safe H2 storage. Carbonaceous nanomaterials (CNMs) have emerged as promising candidates for high-performance H2 storage applications due to their unique properties, including high specific surface area. Despite the inherent advantages of CNMs for H2 storage, challenges remain in their cost-effective synthesis and hydrogen adsorption capacity, especially under ambient conditions. As research advances, a sustainability framework integrating technical, environmental, health, safety, economic, and social aspects is crucial to provide a roadmap to overcome existing barriers against the commercialization of CNMs for hydrogen storage applications. The discussions provided in the present review highlight that adopting strategies including nitrogen-doping, metal (e.g., Pd, Pt, Ni, Ti) incorporation, and the design of CNMs nanocomposites can shape the road of sustainable hydrogen storage. © 2025

关键词： Nanocomposites

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